Environmental Microbiology

„-omics & Quorum Sensing“

Bettina Siebers

Analyses of Microbial Communities

• I. Culture-dependent methods– Enrichment and Isolation

• II. Molecular (Culture-Independent)– (A) Viability and Quantification Using Staining

Techniques– (B) Genetic stains (FISH, chromosome painting, ISRT

fish)– (C) Linking specific genes to specific organisms

using PCR– (D) Environmental Genomics (Metagenomics)

• III. Measuring Microbial Activities in Nature– Radioisotopes (Fish-MAR), microelectrodes, stable

isotopes

Linking specific genes to specific

organisms using PCR

16S rDNA based methods

Averaging methods (provide an overview of diversity but say nothing about the spatial arrangement in the original sample)

● Clone library

● Denaturing gradient gelelectrophoresis (DGGE)

MetagenomicsMolecular community analysis

-Biodiversity of a single gene

versus

Metagenomics-All genes in the microbial community

„metagenome“-Aim: Detect as many genes as possible

and determine to which phylogenetic„scaffold“ they belong.

-Done by sequencing overlaps to the genes that include phylogenetic markers

(e.g. 16S rRNA genes)-Much higher costs but more in-depth

-Avoids „selectivity“ of PCR (primers), all genes are sequenced whether they are

amplifiable or not

Great Potential of Metagenomics!-Detects new genes in known organismsand known genes in new organismsm.

-e.g. genes encoding ammoniamonooxygenase „key enzymes of ammonia-

oxidizing Bacteria“ in Archaea (theseArchaea have never been described!)

Mutations and Evolution• Mutations are important factors of

evolution, because they induce a changein the genome of an organism.

• Environmental conditions decide, which of the spontaneous mutations are positiv ornegativ (= selection).

� Transformation� Transduction � Conjugation

Universal tree of life

Bacteria Eukarya Archaea

Cyano

ba

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Pro

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An

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lia

Fu

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Bacteria Eukarya Archaea

Cyano

ba

cte

ria

Pro

teoba

cte

ria

An

ima

lia

Fu

ng

i

Pla

nta

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Cre

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Pre-genomics Post-genomics

Steps in genomic sequencing

I Library making– Small/Large-insert library from genome

II Production sequencing– Generate fragments to be sequenced– Perform sequencing reactions– Determine sequence

III Finishing– Assemble into continuous sequence– Fill gaps

IV Annotation

Sizes of genomes & numbers of genes�The smallest prokaryotic genomes are the size of the largest viruses, and the largest prokaryotic genomes have more genes than some eukaryotes.

Thermotoga maritima• Metabolic pathways and transport systems (genome analysis)

Hyperthermophile, Bacteria

Genome information: What next?

Genome ( Jan 2009)

-Archaea 55 (101)

-Bacteria 776 (2328)

-Eukarya 100 (1015)

Problems of genome annotation

● Identifying genes and regulatory regions in sequenced genomes is challenging

● Large fraction of genes encoding proteins of unknown function(hypothetical or conserved hypothetical proteins)

● Misannotations „errors“

-annotation procedure

-enzyme families, super- and suprafamilies

Homologs might catalyze different reactions [Gerlt & Babbit, 2000]

● 1,427 characterized enzymes in the protein database (EC #),

encoding genes are unknown

„Functional Genomics“: From gene to function

Genomics provides a parts list

• Provides list of all parts

• Parts list in itself doesn’t say how the genome works

• Can use to get global picture

– e.g., RNA

expression

Functional genomics

• Once we know the sequence of genes, we want to know the function

• The genome is the same in all cells of an individual, except for random mutations

• However, in each cell, only a subset of the genes is expressed

– The portion of the genome that is used in

each cell correlates with the cell’s

differentiated state

„Functional Genomics“-omics= exploration of a special –ome field(-ome = as a whole, totality)Genomics = exploration of the gen-ome, all genes of an organism

-Transcriptomics (transcript)

-Proteomics (protein)

-Metabolomics (metabolit)

-Structural Genomics (protein structure)

-Interactomics (protein interaction)

-Metagenomics (Environmental Genomics, Ecogenomics or Community Genomics)

-Mutagenomics

-XYZ-omics

Metabolome

Proteome

Transcriptome

Gene expression

The ability of a gene to produce a

biologically active protein (e.g. enzyme).

TTT CTTGTT AAT CAG CAT

AAA GAACAA TTA GTC GTA

5´3´

3´5´DNA

TRANSCRIPTION

TRANSLATION

UUU GUU AAU CAG CAU CUUmRNA

PROTEIN

5´ 3´

Phe Val Asn Gln His LeuH2N- -COOH

Stored information

Intermediary

Functionalunit

Brock

transcriptomics

proteomics

„Functional Genomics“

Yanai 2002

Classical approaches:Analysis of gene (gene group) function,

involved in certain processes „hypothesis driven“

vs

„Molecular biology in 96-well format“Analysis of genome function

(all genes of an Organism)

TranscriptomicsRNA Expression Analysis

Determining genomewide RNA expression levels

Genomewide expression analysis

• Transcriptome: All transcripts (mRNA) in a special cell type under certain environmental/growth conditions or respectively developmental stage

• Goal: to measure mRNA levels of all genes in genome

• RNA levels vary with the following:

– Cell type

– Developmental stage

– External stimuli

• Snapshot of all transcribed genes

• Time and location of expression provide useful information as to gene function

Spotted-microarray hybridization

• Control and experimental cDNA labeled (reverse transcription)

– One sample labeled with Cy3

– Other sample labeled with Cy5

• Both samples hybridized together to microarray

• Relative intensity determined using confocal laser scanner

• Laser beam excites each spot of DNA

• Amount of fluorescence detected

• Different lasers used for different wavelengths (Cy3/Cy5)

cDNA Microarray hybridization

• Usually comparative

– Ratio between two samples

• Examples

– CO2 vs. sugar

– Drug treatment vs. no treatment

– log vs. stationary phase

mRNA

cDNA

DNAmicroarray

samples

Analysis of hybridization

• Results given as

ratios

• Images use colors:

Cy3 = Green

Cy5 = red

Yellow

– Yellow is equal intensity or no change in expression

Transcriptomics

Microarray Data Mining

Hierarchical clustering of gene expression

-places genes n clusters with similar expression profile (Eisen et al. 1998)

-clustering implies co-regulation and may imply involvement in similar biological processes

Proteomics

Using high-throughput methods to identify proteins and to

understand their function

What is proteomics?

• An organism’s proteome

– A catalog of all proteins

• Expressed throughout life

• Expressed under all conditions

• The goals of proteomics

– To catalog all proteins

– To understand their functions

– To understand how they interact with each

other

The challenges of proteomics

• Splice variants create an enormous diversity of proteins– ~25,000 genes in humans give rise to 200,000 to 2,000,000

different proteins

– Splice variants may have very diverse functions

• Variable expression profile; Proteins expressed in an organism will vary according to age, health, tissue, and environmental stimuli

• Proteomics requires a broader range of technologies than genomics

Technologies for proteomics

• 2-D gel electrophoresis– Separates proteins in a mixture on the basis of their

molecular weight and charge

• Mass spectrometry– Reveals identity of proteins

• Protein chips– A wide variety of identification methods

• Yeast two-hybrid method– Determines how proteins interact with each other

• Biochemical genomics– Screens gene products for biochemical activity

2-D Gel Electrophoresis

Lodish 5e

● First dimension: Isoelectric focussing (IEF)

- Cellular proteins are separated in the

first dimension by their isoelectric point (IEP) „charge“.

- Polyacrylamide gel (strip) with

immobilized pH gradient.

-Proteins will migrate until they reach

the pH where they lose their net

charge (= IEP).

● Second dimension: Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)- Cellular proteins are separated by their molecular weight (denaturing conditions !).

2-D Gel Electrophoresis

Lodish 5e

Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)-Separation of denatured proteins according to their molecular weight

2-D Gel Electrophoresis

Lodish 5e

Differential in gel electrophoresis

• Label protein samples from control and experimental tissues– Fluorescent dye #1 for

control

– Fluorescent dye #2 for experimental sample

• Mix protein samples together

• Identify identical proteins from different samples by dye color

withbenzoicacidCy3

withoutbenzoicacidCy5

Problems associated with 2-D gels

• Poor performance of 2-D gels for the following:

– Very large proteins

– Very small proteins

– Less abundant proteins (e.g. transcription

factors)

– Membrane-bound proteins

• Presumably, the most promising drug targets

Mass spectrometry

• Measures mass-to-

charge ratio

• Components of mass

spectrometer

– Ion source

– Mass analyzer

– Ion detector

– Data acquisition unitA mass spectrometer

Identifying proteins with mass spectrometry

• Preparation of protein sample– Extraction from a gel

– Digestion by proteases — e.g., trypsin

• Mass spectrometer measures mass-charge ratio of peptide fragments

• Identified peptides are compared with database

– Software used to generate theoretical peptide mass fingerprint (PMF) for all proteins in database

– Match of experimental readout to database PMF allows researchers to identify the protein

A mass spectrum

Stable-isotope protein labeling

• Stable isotopes used to label proteins under different conditions

• Variety of labeling methods– Enzymatic

– Metabolic

– Via chemical reaction

• Relative abundance of labeled and nonlabeled proteins measured in mass spectrum

• iTRAQ (isobaric tag for relative and absolute quantitation)

• is a non-gel based technique

• identifies and quantifies proteins from different sources in one single experiment

• iTRAQ uses isobaric labels

• Varies only between the mass of ‘Reporter’ and ‘Balance’

Reporter Balance RXN

Isobaric

Reporter Mass Balance Mass

113114115116117118119121

192191190189188187186184

~~~~~~~~

Total

iTRAQ 8-Plex tag

R BAL C

Reporter Balance Rxn group

R BAL C

R BAL CSAMPLE

Tandem MS Fragmentation

Reporter Ions

iTRAQ methodology

Limitations of mass spectrometry

• Not very good at identifying minute quantities of protein

• Trouble dealing with phosphorylated proteins

• Does not provide concentrations of proteins

• Improved software eliminating human analysis is necessary for high-throughput projects

Protein chips

• Thousands of proteins analyzed simultaneously

• Wide variety of assays

– Antibody–antigen

– Enzyme–substrate

– Protein–small molecule

– Protein–nucleic acid

– Protein–protein

– Protein–lipid

Yeast proteins detectedusing antibodies

Biochemical genomics

• Genome of an organism is already known

• Approach

– Construct plasmids for all ORFs

• Attach ORFs to sequence that will facilitate purification

– Transform cells

– Isolate ORF products

– Test for biochemical activity

Microfluidics

• Proteomics requires

greater automation

• Microfluidics: a “lab

on a chip”

– Microvalves and pumps allow control of nanoliter amounts

– Can control biochemical reactions

A microfluidics chip

Microfluidics in action

loading compartmentalization

purgingmixing

500 µm

Systems Biology

Hierarchies of organization

• Level 1: genes, RNA,

proteins, metabolites

• Level 2: pathways

• Level 3: functional

modules

• Level 4: networks

Parts, modules, networks

26" wheels,alloy rims,handcrafted aluminum frame,dual-suspension,downhill front fork,alloy crank,21-speed grip shift system,quick release seat, linear pull brakes,Shimano® derailers,crank and rear sprocket

Parts list Module Network

Systems biology

• Goal: describe complex cellular systems

• Integrative approach (Bioinformatics, Mathematics, Biologists, Engineers etc.)

• Uses approach of systems engineering “Modeling”– Sees complex processes in terms of circuits

• Inputs

• Outputs

• Dynamics

• Testing the system– Simulations

– Perturbations

• “Communication”

https://www.im.org/AAIM/Meetings/PastMeetings/2006/APDIM/PlenaryIV-BabyatskyMark-ANewScientificLiteracyforClinicians.pdf

„Quorum Sensing“

Communication between Bacteria

Quorum sensing

• Bacteria are able to communicate using signalling molecules released into the environment.

• Bacteria are able to sense the number of bacteria present (cell density) by the level of accumulation of signal molecules. – The more bacteria present the more signal.

• In this way, bacteria are able to regulate their gene expression in response to alterations in cell density

?From the bacterial point of view: Who are my neighbors and what are they going to do?

Modified, J. Czichos

Quorum sensing

• Quorum sensing enables bacteria to co-ordinate their gene expression.

• Quorum sensing controls various different activities in different bacteria.– Luminescence

– Virulence

– Production of extracellular enzymes

– Plasmid transfer, genetic competence

– Antibiotic synthesis

– Motility

– Sporulation

– Symbiosis

– Biofilm development

Quorum Sensing

• There are different types of quorum sensing mechanisms:– Peptides or modified peptides are the main

signalling molecule for Gram positive bacteria

– Autoinducer 1 – acylated homoserine lactone signals used by Gram negative bacteria for intraspecies communication.

– Autoinducer 2 – furanone signals used by Gram negative and Gram positive bacteria for interspecies communication.

– Autoinducer 3 – signal controlling virulence in Enterohemorrhagic E. coli.

• using acylated homoserine lactones (AHL)

• Different gram negative bacteria produce different quorum sensing AI-1 signal molecules.

γ-butryolactone from Streptomyces griseus 3-oxo-C6-HSL from Vibrio fischeri

2-heptyl-3-hydroxy-4-quinolone from Pseudomonas aeruginosa

Autoinducer 1 - Quorum sensing

O

OOH

H

O

O O

HO

ON

H

O

N

H

HO

Quorum sensing in Vibrio fischeri

• Quorum sensing was first discovered as a form of regulation of bioluminescence in Vibrio fischeri.

– Gram negative, marine, bioluminescent, symbiotic Bacteria

• The lux operon encodes genes involved in bioluminescence. LuxI/LuxR type quorum sensing

• The picture shows colonies glowing because of production of bacterial luciferase.

The Hawaiian bobtailed squid

● Cephalopoda, Sepiolida, Sepiolidae

- marine (Hawaii)

- seize: 3,5 cm

- life time: 3-10 months

- shallow waters (2-4 cm depths)

- distinct day-neight rhythm

http://www.dal.ca/~ceph/

TCP/Escolopes.html

Euprymna scolopes

Symbiosis between Vibrio fischeri

and the Hawaiian bobtailed squid

• Vibrio fischeri colonise an internal organ (the light organ) of the squid where they reach high enough density to cause expression of the lux genes, resulting in bioluminescence. Hastings & Nealson, 1977

Symbiosis- Differenciation

- Light organ (1010-1011 V. fisheri cells/ml)

- Marine water (<102 V. fisheri cells/ml)

- Host: Camouflage

(protection against hunters)

- Symbiont: protection, food

V. fisheri Luminescence

http://www.biology.pl/bakterie_

sw/bakterie_hp.html

Luciferase reaction

FMNH2 + RCHO + O2 → FMN + RCOOH + H2O + Licht (490 nm)

(Oxidatio of long-chain aldehydes (RCHO) and reduced flavin

mononucleotide (FMNH2))

V. fisheri Luminescence

K. H. Nealson et al. 1970

Cell density

Luminescence

Luciferase

What is the Signal ?

V. fisheri

„Low cell density“

Culture-supernatant

„High cell density“

http://mcb1.ims.abdn.ac.uk/staff/lag.html

5 h

„Low cell density“

K. H. Nealson et al. 1970

A. Eberhard 1972

The Signal „Autoinducer“

V. fisheri

N-(3-oxohexanoyl) homoserine-lactone

A. Eberhard et al. 1981

Biosynthesis:

Acyl-ACP + S-Adenosylmethionin → Acyl-Homoserin-Lacton + Methylthioadenosin + ACP

A. Eberhard et al. 1991

J.-G. Cao & E. A. Meighen 1993

Biosynthesis of AI-1

Eberhard A. et al. 1991

Cao J-G & Meighen E. A.1993

1) Formation of amide linkage

2) Lactonisation, release of methylthioadenosine

3) Formation of the acylated homoserine lactone

ACP = acyl-carrier protein

Biosynthesis:

Acyl-ACP + S-Adenosylmethionin → Acyl-Homoserin-Lacton + Methylthioadenosin + ACP

LuxI/LuxR-Typ QS in V. fisheri

LuxI

Acyl-homoserine-lactone (AHL) synthase

Synthesis of the „Autoinducer“

LuxR

Transcriptionfactor „activator“

Binds autoinducer and influences transcription

Lux structural genes

luxI C D A B EluxR P O P

Fettsäure Reduktase

Luciferase

LuxI/LuxR-Typ „QS“

LuxI

AI-1

LuxR

luxI C D A B EluxR

Quorum sensing in Vibrio fischeri

• At low cell densities the lux operon (luxICBADE) is expressed at a low level and small amounts of 3-oxo-C6-HSL produced diffuse out of the cell.

• The luxCDABE genes are responsible for bioluminescence.

luxRlux

boxluxI C D A B E

LuxR LuxIGenes responsible

for bioluminescence

3-oxo-C6-HSL

inactiveactivator

Low cell density

Quorum sensing in Vibrio fischeri

• At high cell densities 3-oxo-C6-HSL accumulates in the environment and within the cell.

• Transcription of luxICDABE increases when the complex of LuxR and 3-oxo-C6-HSL (the active activator) binds to the lux box.

• LuxR-autoinducer complex represses transcription of LuxR (negative action compensates for positive action of LuxICDABE promoter)

luxRlux

boxluxI C D A B E

LuxR LuxI

3-oxo-C6-HSL

inactiveactivator

activeactivator

LIGHT

High cell density

AI-1 quorum sensing systems of other bacteria and the phenotype they control

bacteriumluxR/I homologues

Major AHL phenotype

Aeromonas

hydrophila

AhyR, AhyI C4-HSL Extracellular

protease, biofilm

formation

Agrobacterium

tumefaciens

TraR, TraI 3-oxo-C8-HSL Conjugation

Pseudomonas

aeruginosa

LasR, LasI 3-oxo-C12-HSL Exoenzymes,

biofilm formation, cell-cell spacing,

Twitching motility

Pseudomonas

aurofaciens

PhzR, PhzI C6-HSL Phenazineantibiotic

AHL = Acylated homoserine lactone

Specificity?● > 70 LuxI/LuxR-type QS systems (Gram-negative Bacteria)

Species-specific communication

● Substrate specificity of LuxI-similar proteins (acyl-residue)

● Binding specificity or LuxR-similar proteins for specific autoinducer

Acylated-homoserine-lactones V. fisheri /LuxI

P. aeruginosa /LasI

P. aeruginosa /RhlI

A. tumefaciens /TraI

V. harveyi /LuxLM

S. Schauder & B. L. Bassler 2001

LuxI/LuxR Homologs

Pseudomonas aeruginosa

● Opportunistic pathogen

a) LasI/LasR

b) RhlI/RhlR

● Biofilm formation

● Cell-associated and extracellular virulence factors

„Necessary number for effective infection“

LuxI/LuxR HomologeErwinia carotovora

● Plant-Pathogen (bacterial soft rot (BSR) „Weichfäule“)

CarI/CarR

● Antibiotics

● Exoenzymes

Agrobacterium tumefaciens

● Plant-Pathogen (crown-gall disease, „Wurzelhals Tumore“)

TraI/TraR

● Conjugation

LuxI/LuxR Homologe

Rhizobium leguminosarum

● Plant-symbiont

CinI/CinR

RhiI/RhiR

BisR

TriR

● Nodulation

● Bacteriocin